Delete README_WEIGHTS.md

README_WEIGHTS.md

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-# DeepSeek-V3 Weight File Documentation
-
-## New Fields in `config.json`
-
-- **model_type**: Specifies the model type, which is updated to `deepseek_v3` in this release.
-- **num_nextn_predict_layers**: Indicates the number of Multi-Token Prediction (MTP) Modules. The open-sourced V3 weights include **1 MTP Module** .
-- **quantization_config**: Describes the configuration for FP8 quantization.
-
----
-
-## Weight Structure Overview
-
-The DeepSeek-V3 weight file consists of two main components: **Main Model Weights** and **MTP Modules**.
-
-### 1. Main Model Weights
-
-- **Composition**:
-  - Input/output embedding layers and a complete set of 61 Transformer hidden layers.
-- **Parameter Count**:
-  - Total parameters: **671B**
-  - Activation parameters: **36.6B** (including 0.9B for the output Head).
-
-#### Structural Details
-
-- **Embedding Layer**:
-  - `model.embed_tokens.weight`
-- **Transformer Hidden Layers**:
-  - `model.layers.0` to `model.layers.60`, totaling `num_hidden_layers` layers.
-- **Output Layer**:
-  - `model.norm.weight`
-  - `lm_head.weight`
-
-### 2. Multi-Token Prediction (MTP) Modules
-
-- **Composition**:
-  - Additional MTP Modules defined by the `num_nextn_predict_layers` field. In this model, the value is set to 1.
-- **Parameter Count**:
-  - Parameters: **11.5B unique parameters** (excluding the shared 0.9B Embedding and 0.9B output Head).
-  - Activation parameters: **1.5B** (including 0.9B for the output Head).
-
-#### Structural Details
-
-- **embed_tokens**: **Shares parameters** with the Embedding layer of the Main Model weights.
-- **enorm & hnorm**: RMSNorm parameters required for speculative decoding.
-- **eh_proj**: Parameters for dimensionality reduction projection on the norm results.
-- **Additional Transformer Hidden Layer**:
-  - `model.layers.61.self_attn & mlp` (structure identical to the Main Model hidden layers).
-- **shared_head**: **Shares parameters** with the output Head of the Main Model weights.
-
----
-
-### Loading Rules
-
-- **Main Model Weights**: Loaded via the `num_hidden_layers` parameter in `config.json`.
-- **MTP Modules**: Loaded via the `num_nextn_predict_layers` parameter, with layer IDs appended immediately after the Main Model hidden layers. For example:
-  - If `num_hidden_layers = 61` and `num_nextn_predict_layers = 1`, the MTP Module's layer ID is `61`.
-
----
-
-## FP8 Weight Documentation
-
-DeepSeek-V3 natively supports FP8 weight format with 128x128 block scaling.
-
-### FP8 Configuration
-
-The FP8 weight file introduces a `quantization_config` field to describe the quantization method. Below is an example configuration:
-
-```json
-"quantization_config": {
-  "activation_scheme": "dynamic",
-  "fmt": "e4m3",
-  "quant_method": "fp8",
-  "weight_block_size": [128, 128]
-}
-```
-
-- **Quantization Format**:
-  - Format type: `fp8` and `e4m3` (corresponding to `torch.float8_e4m3fn`).
-  - Weight block size: `128x128`.
-- **Activation Quantization Scheme**:
-  - Utilizes dynamic activation quantization (`dynamic`).
-
-### Dequantization Method
-
-The FP8 weight file includes a `weight_scale_inv` field, which stores the dequantization scale for each weight block.
-
-- **Storage Format**: `float32 Tensor`, stored alongside the weight data.
-- **Dequantization Formula**:
-  - If the weight block is not aligned to 128, it is zero-padded to 128 before calculating the scale. After quantization, the padded portion is removed.
-  - The dequantization process is performed as: `(128x128 weight block) * weight_scale_inv`.
-
-Through dequantization of the FP8 weights, runtime operations enable online quantization at a granularity of `per-token-per-128-channel`.
-
----